



import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version

def 	SCREAMING_SNAKE_CASE_							(	*_snake_case	:Optional[Any]	,   _snake_case	:Optional[Union[Dict, Any]] = None	,   _snake_case	:Optional[int]=True	,   _snake_case	:Optional[int]=2				)  ->     str:
   from .. import __version__

   _A      =       take_from
   _A      =       ()
   if not isinstance(args[0]	,   _snake_case				):
      _A      =       (args,)

   for attribute, version_name, message in args:
      if version.parse(version.parse(_snake_case				).base_version				) >= version.parse(_snake_case				):
         raise ValueError(
             F'''The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''''
             F''' version {__version__} is >= {version_name}'''				)

      _A      =       None
      if isinstance(_snake_case	,   _snake_case				) and attribute in deprecated_kwargs:
         values += (deprecated_kwargs.pop(_snake_case				),)
         _A      =       F'''The `{attribute}` argument is deprecated and will be removed in version {version_name}.'''
      elif hasattr(_snake_case	,   _snake_case				):
         values += (getattr(_snake_case	,   _snake_case				),)
         _A      =       F'''The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'''
      elif deprecated_kwargs is None:
         _A      =       F'''`{attribute}` is deprecated and will be removed in version {version_name}.'''

      if warning is not None:
         _A      =       warning + ''' ''' if standard_warn else ''''''
         warnings.warn(warning + message	,   _snake_case	,   stacklevel=_snake_case				)

   if isinstance(_snake_case	,   _snake_case				) and len(_snake_case				) > 0:
      _A      =       inspect.getouterframes(inspect.currentframe()				)[1]
      _A      =       call_frame.filename
      _A      =       call_frame.lineno
      _A      =       call_frame.function
      _A  ,    _A      =       next(iter(deprecated_kwargs.items()				)				)
      raise TypeError(F'''{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'''				)

   if len(_snake_case				) == 0:
      return
   elif len(_snake_case				) == 1:
      return values[0]
   return values





"""simple docstring"""



import math




class 		a_    :







    def   _snake_case       (      self      :							List[Any]  ,       __UpperCamelCase      :							list[list[float]]  ,       __UpperCamelCase      :							list[int]  )		->int:






        '''simple docstring'''






        _UpperCAmelCase        = 0.0
        _UpperCAmelCase        = 0.0
        for i in range(len(__UpperCamelCase  )  ):
            da += math.pow((sample[i] - weights[0][i])  ,       2  )
            da += math.pow((sample[i] - weights[1][i])  ,       2  )
            return 0 if da > da else 1
        return 0







    def   _snake_case       (      self      :							Dict  ,       __UpperCamelCase      :							list[list[int | float]]  ,       __UpperCamelCase      :							list[int]  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							float  )		->list[list[int | float]]:






        '''simple docstring'''






        for i in range(len(__UpperCamelCase  )  ):
            weights[j][i] += alpha * (sample[i] - weights[j][i])
        return weights







def 			_UpperCamelCase						(    )     ->	None:





    """simple docstring"""

    _UpperCAmelCase        = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

    # weight initialization ( n, C )
    _UpperCAmelCase        = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

    # training
    _UpperCAmelCase        = SelfOrganizingMap()
    _UpperCAmelCase        = 3
    _UpperCAmelCase        = 0.5

    for _ in range(_A   ):
        for j in range(len(_A   )   ):
            # training sample
            _UpperCAmelCase        = training_samples[j]

            # Compute the winning vector
            _UpperCAmelCase        = self_organizing_map.get_winner(_A       ,   _A   )

            # Update the winning vector
            _UpperCAmelCase        = self_organizing_map.update(_A       ,   _A       ,   _A       ,   _A   )

    # classify test sample
    _UpperCAmelCase        = [0, 0, 0, 1]
    _UpperCAmelCase        = self_organizing_map.get_winner(_A       ,   _A   )

    # results
    print(F"""Clusters that the test sample belongs to : {winner}"""   )
    print(F"""Weights that have been trained : {weights}"""   )


# running the main() function
if __name__ == "__main__":
 main()